Unsupervised Pattern Analysis in Japanese Veterinary Toxicology: A Regulatory-Compliant Framework for Cross-Species Risk Assessment

• URL: http://arxiv.org/abs/2606.06207v1
• Date: Thu, 04 Jun 2026 14:14:16 GMT
• Title: Unsupervised Pattern Analysis in Japanese Veterinary Toxicology: A Regulatory-Compliant Framework for Cross-Species Risk Assessment
• Abstract summary: This study proposes a regulatory-integrated unsupervised framework for pattern discovery using the National Veterinary Assay Laboratory (NVAL) database.<n>Similarity-based clustering and dimensionality reduction are applied to identify latent toxicity structures.<n>Analysis of 4,120 high-confidence ADE reports identified three significant species clusters.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Veterinary pharmacovigilance systems are essential for monitoring adverse drug events (ADEs), yet existing approaches often fail to capture region-specific toxicity patterns shaped by local biological and regulatory contexts. In Japan, these challenges are amplified by species-specific metabolic differences and reporting practices defined by the Ministry of Agriculture, Forestry, and Fisheries (MAFF). Most prior work relies on prediction-oriented models, limiting mechanistic interpretability. This study proposes a regulatory-integrated unsupervised framework for pattern discovery using the National Veterinary Assay Laboratory (NVAL) database. ADEs are encoded into organ system-aligned representations and adjusted for species-specific reporting biases, enabling cross-species comparison. Similarity-based clustering and dimensionality reduction are applied to identify latent toxicity structures. Analysis of 4,120 high-confidence ADE reports (9,080 drug-ADE combinations) identified three significant species clusters (p < 0.01), including hepatic-dominant patterns in companion animals (0.42 $\pm$ 0.06), renal toxicity in ruminants (0.39 $\pm$ 0.07), and dermatological sensitivity in sheep (0.35 $\pm$ 0.07). Drug-level clustering achieved 83% alignment with pharmacological classes, while cosine similarity outperformed alternative metrics (silhouette score: 0.48; cluster precision: 87%). Regulatory validation showed strong agreement with established classifications. These findings demonstrate that regulation-aligned unsupervised analysis can uncover biologically meaningful, region-specific toxicity patterns, providing an interpretable and scalable framework for veterinary drug safety assessment.

Related papers

• Cross-Species Animal Re-Identification with Semantic Consistency Learning [49.843830441987684]
  Generalizable animal Re-Identification (ReID) aims to recognize individual animals across species with diverse morphologies and ecological contexts.<n>Unlike person ReID, animal species often exhibit drastically different anatomical structures and visual patterns, making it difficult to establish shared visual correspondences.<n>We propose Semantic Consistency Learning (SCL), a framework designed to learn representations that remain stable across appearance variations.
  arXiv  Detail & Related papers  (2026-09-09T04:41:01Z)
• An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES [0.0]
  Drug-induced liver injury (DILI) remains a leading cause of late-stage clinical trial attrition.<n>We argue that DILI prediction is better posed as an explainable hypothesis-generation problem.<n>We present HADES, an agentic system designed to generate transparent and auditable reasoning traces.
  arXiv  Detail & Related papers  (2026-05-04T14:50:28Z)
• Eligibility-Aware Evidence Synthesis: An Agentic Framework for Clinical Trial Meta-Analysis [49.83108591873481]
  EligMeta is an agentic framework that integrates automated trial discovery with eligibility-aware meta-analysis.<n>The framework structures eligibility criteria and computes similarity-based study weights reflecting population alignment between target trials.
  arXiv  Detail & Related papers  (2026-04-03T03:18:50Z)
• Development of a fully deep learning model to improve the reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction [0.11726720776908518]
  The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator of sector classification systems.<n>DenseNet121 proved to be the best-performing model in allocating impacted in the three different classes, with an overall accuracy of 76.8%.
  arXiv  Detail & Related papers  (2025-11-24T14:45:46Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• Assessing the Feasibility of Early Cancer Detection Using Routine Laboratory Data: An Evaluation of Machine Learning Approaches on an Imbalanced Dataset [0.02030567625639093]
  The development of accessible screening tools for early cancer detection in dogs represents a significant challenge in veterinary medicine.<n>This study assesses the feasibility of cancer risk classification using the Golden Retriever Lifetime Study cohort under real-world constraints.<n>It is concluded that while a statistically detectable cancer signal exists in routine lab data, it is too weak and confounded for clinically reliable discrimination from normal aging or other inflammatory conditions.
  arXiv  Detail & Related papers  (2025-10-23T04:52:42Z)
• Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective [104.09817371557476]
  Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks.<n>Their potential to generate harmful content has raised serious safety concerns.<n>We introduce three novel multi-label benchmarks for toxicity detection.
  arXiv  Detail & Related papers  (2025-10-16T06:50:33Z)
• Decoding Positive Selection in Mycobacterium tuberculosis with Phylogeny-Guided Graph Attention Models [0.17499351967216337]
  Positive selection drives the emergence of adaptive mutations in Mycobacterium tuberculosis.<n>We present a phylogeny-guided graph attention network (GAT) approach for converting SNP-annotated phylogenetic trees into graph structures suitable for neural network analysis.
  arXiv  Detail & Related papers  (2025-10-09T18:06:39Z)
• Predictive Modeling and Explainable AI for Veterinary Safety Profiles, Residue Assessment, and Health Outcomes Using Real-World Data and Physicochemical Properties [4.53318808068234]
  Adverse events (AEs) may signal unexpected or toxicokinetic effects, increasing the risk of violative residues in the food chain.<n>This study introduces a predictive framework for classifying outcomes (Death vs. Recovery) using 1.28 million reports from the U.S. FDA's OpenFDA Center for Veterinary Medicine.
  arXiv  Detail & Related papers  (2025-10-01T23:34:46Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.